- Full Description
'Foundations of Large-Scale Multimedia Information Management and Retrieval: Mathematics of Perception' covers knowledge representation and semantic analysis of multimedia data and scalability in signal extraction, data mining, and indexing. The book is divided into two parts: Part I - Knowledge Representation and Semantic Analysis focuses on the key components of mathematics of perception as it applies to data management and retrieval. These include feature selection/reduction, knowledge representation, semantic analysis, distance function formulation for measuring similarity, and multimodal fusion. Part II - Scalability Issues presents indexing and distributed methods for scaling up these components for high-dimensional data and Web-scale datasets. The book presents some real-world applications and remarks on future research and development directions. The book is designed for researchers, graduate students, and practitioners in the fields of Computer Vision, Machine Learning, Large-scale Data Mining, Database, and Multimedia Information Retrieval.Dr. Edward Y. Chang was a professor at the Department of Electrical & Computer Engineering, University of California at Santa Barbara, before he joined Google as a research director in 2006. Dr. Chang received his M.S. degree in Computer Science and Ph.D degree in Electrical Engineering, both from Stanford University.
- Table of Contents
Table of Contents
- Part I
- Knowledge Representation and Semantic Analysis.
- 1. Mathematics of Perception.
- 2. Supervised Learning (based on tutorial DASFAA 2003).
- 3. Query Concept Learning (based on IEEE TMM 2005).
- 4. Feature Extraction.
- 5. Feature Reduction (based on MM 04, ICME 05, IPAM).
- 6. Similarity (based on MMJ 2002, CIKM 04, ICML 05).
- Part II
- Scalability Issues.
- 7. Imbalanced Data Learning (based on TKDE 2005).
- 8. Semantics Fusion (based on MM 04, MM05, KDD 08).
- 9. Kernel Machines Speedup (based on SDM 05, KDD 06, NIPS 07).
- 10. Kernel Indexing (based on TKDE 06).
- 11. Put It All Together (based on SPIE 06).
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